Project Please follow instructions

Final Project Wednesday, December 6th at 11:59pmGeneral instructions:
Throughout this course, you have demonstrated your ability to apply a variety of exploratory and
statistical learning approaches in Python to address diverse questions in data science.
Hence, this final project will evaluate your ability to communicate findings from a data science project in
a written report with more emphasis on analysis.
Your final paper should describe the application of statistical learning approaches discussed in class to a
Kaggle dataset or similar. kaggle at https://www.kaggle.com/datasets.
Formatting requirements:
• At least 4 pages (5 pages if including a header with your name, class, title, etc.)
• Double-spaced
• One-inch margins on all sides
• 11-point Calibri font
• You can share your code on a separated appendix.
Content requirements:
• Name and website address of the kaggle dataset that you chose to analyze.
• Description of the dataset (e.g., what it is, numbers of observations and features, variable types,
etc.)
• Problem being addressed (e.g., predicting 𝑌 from 𝑋, clustering)
• Approach taken to address the problem (e.g., linear regression, polynomial, decision tree, SVM,
other)
• Findings of all analyses (e.g., statistical metrics)
• Figure that supports analyses (e.g., scatter plot, box plot, other, etc.)
• Interpretations of each finding (e.g., “The data support that there is a linear relationship
between…”)
• Strengths and limitations of the approach (e.g., model fit, interpretability, prediction error, etc.)
Grading will be based on adherence to formatting and content requirements, as well as on effective
communication. Points will be deducted for any perceived attempts to circumvent formatting
requirements, including the use of excessively large figures or tables. Also ensure that figures are as clear
and professional as possible. This means that they should be exported from Python and then inserted as
pictures, and that axes should be appropriately labeled. Remember that the key goal of this report is to
effectively communicate findings, and that data visualization can impart the opposite effect if it is
unclear.